Vaccination is the most successful and cost-effective method to prevent infectious diseases. However, many vaccine antigens have poor in vivo immunogenic potential and need adjuvants to enhance immune response. The application of systems biology to immunity and vaccinology has yielded crucial insights about how vaccines and adjuvants work. We have previously characterized two safe and powerful delivery systems derived from non-pathogenic prokaryotic organisms: E2 and fd filamentous bacteriophage systems. They elicit an in vivo immune response inducing CD8+ T-cell responses, even in absence of adjuvants or stimuli for dendritic cells' maturation. Nonetheless, a systematic and comparative analysis of the complex gene expression network underlying such activation is missing. Therefore, we compared the transcriptomes of ex vivo isolated bone marrow-derived dendritic cells exposed to these antigen delivery systems. Significant differences emerged, especially for genes involved in innate immunity, co-stimulation, and cytokine production. Results indicate that E2 drives polarization toward the Th2 phenotype, mainly mediated by Irf4, Ccl17, and Ccr4 over-expression. Conversely, fd-scalphaDEC-205 triggers Th1 T cells' polarization through the induction of Il12b, Il12rb, Il6, and other molecules involved in its signal transduction. The data analysis was performed using RNASeqGUI, hence, addressing the increasing need of transparency and reproducibility of computational analysis.

We present the advancements and novelties recently introduced in RNASeqGUI, a graphical user interface that helps biologists to handle and analyse large data collected in RNA-Seq experiments. This work focuses on the concept of reproducible research and shows how it has been incorporated in RNASeqGUI to provide reproducible (computational) results. The novel version of RNASeqGUI combines graphical interfaces with tools for reproducible research, such as literate statistical programming, human readable report, parallel executions, caching, and interactive and web-explorable tables of results. These features allow the user to analyse big datasets in a fast, efficient, and reproducible way. Moreover, this paper represents a proof of concept, showing a simple way to develop computational tools for Life Science in the spirit of reproducible research.

The human retina is a specialized tissue involved in light stimulus transduction. Despite its unique biology, an accurate reference transcriptome is still missing. Here, we performed gene expression analysis (RNA-seq) of 50 retinal samples from non-visually impaired post-mortem donors. We identified novel transcripts with high confidence (Observed Transcriptome (ObsT)) and quantified the expression level of known transcripts (Reference Transcriptome (RefT)). The ObsT included 77 623 transcripts (23 960 genes) covering 137 Mb (35 Mb new transcribed genome). Most of the transcripts (92%) were multi-exonic: 81% with known isoforms, 16% with new isoforms and 3% belonging to new genes. The RefT included 13 792 genes across 94 521 known transcripts. Mitochondrial genes were among the most highly expressed, accounting for about 10% of the reads. Of all the protein-coding genes in Gencode, 65% are expressed in the retina. We exploited inter-individual variability in gene expression to infer a gene co-expression network and to identify genes specifically expressed in photoreceptor cells. We experimentally validated the photoreceptors localization of three genes in human retina that had not been previously reported. RNA-seq data and the gene co-expression network are available online (http://retina.tigem.it).

Reproducible (computational) Research is crucial to produce transparent and high quality scientific papers. First, we illustrate the benefits that scientific community can receive from the adoption of Reproducible Research standards in the analysis of high-throughput omic data. Then, we describe several tools useful to researchers to increase the reproducibility of their works. Moreover, we face the advantages and limits of reproducible research and how they could be addressed and solved. Overall, this paper should be considered as a proof of concept on how and what characteristic - in our opinion - should be considered to conduct a study in the spirit of Reproducible Research. Therefore, the scope of this paper is two-fold. The first goal consists in presenting and discussing some easy-to-use instruments for data analysts to promote reproducible research in their analyses. The second aim is to encourage developers to incorporate automatic reproducibility features in their tools.

In this paper, we discuss the concept of Reproducible Research and its importance to produce transparent and high quality scientific papers. In particular, we illustrate the advantages that both paper authors and readers can receive from the adoption of Reproducible Research and we discuss a strategy to develop computational tools supporting such a feature. We present a novel version of RNASeqGUI, a user friendly computational tool capable to handle and analyse RNA-Seq data. This tool exploits Reproducible Research feature to produce RNA-Seq analyses easy to read, inspect, understand, study, reproduce and modify. Overall, this paper is a proof of concept on how it is possible to develop complex and interactive tools in the spirit of Reproducible Research.

Summary: We present RNASeqGUI R package, a graphical user interface (GUI) for the identification of differentially expressed genes across multiple biological conditions. This R package includes some wellk-nown RNA-Seq tools, available at www.bioconductor.org. RNASeqGUI package is not just a collection of some known methods and functions, but it is designed to guide the user during the entire analysis process. RNASeqGUI package is mainly addressed to those users who have little experience with command-line software. Therefore, thanks to RNASeqGUI, they can conduct analogous analyses using this simple graphical interface. Moreover, RNASeqGUI is also helpful for those who are expert R-users because it speeds up the usage of the included RNASeq methods drastically.